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MS Noise designs and manufactures high-performance soundproofing enclosures. MRI rooms, data centers, pharmaceutical labs, petrochemical plants. Noise reduction up to 80%. A catalog covering 13 industries and serving customers across four continents.
The website runs in five languages: English, French, Japanese, Spanish, and German. On paper, solid infrastructure. Inside the responses generated by ChatGPT, Perplexity, or Gemini in March 2026, a brand that was hard to interpret correctly.
The challenge: a site built for humans, opaque to AI
The B2B buying journey has shifted to generative engines.
A lab manager looking for a soundproofing solution for a vacuum pump no longer starts with Google. They query ChatGPT. They validate on Perplexity. They compare on Gemini. They only go back to classic search to make the final call.
Along that journey, MS Noise had to be findable, interpretable, and citable everywhere. Not just in the blue links.
Three blind spots were hurting both Google and the LLMs:
- No structured data. Crawlers had to guess which product, which compatible brand, which language, which industry.
- No usable breadcrumbs. No clear path to navigate product → category → industry.
- Overloaded product pages. Long specification lists that drowned the key information above the fold.
The key insight: a technically clean website can still stay unreadable to AI if it doesn't expose its data in the right format.
Our approach: three workstreams, 72 hours
The mission fit in one sentence: get the website ecosystem ready for generative search.
Not a single deliverable. Three workstreams, run in parallel, shipped in 72 hours.
1. Structured data wired to the CMS. A full layer of Schema.org markups, hooked directly into Webflow CMS variables so every new piece of content inherits its markup automatically.
2. Dynamic multilingual breadcrumbs. Rolled out on strategic pages, with two visual variants (light / dark background), translated across all five languages, connected to CMS items.
3. Product page UI. Load-more logic on long specification lists. A clean first view, and a readable hierarchy for search engines.
Structured data: 85 markups deployed across 5 languages
The technical core. Three families of markups, all wired to the Webflow CMS so new content inherits the markup automatically.
65 BreadcrumbList markups. The 10 static pages (Products, Brands, Industries, Resources, About Us, Contact, Legals) each have a custom breadcrumb per language, with translated labels and localized URLs matching the sitemap. Alongside them, 4 CMS templates (Products, Brands, Industries, Articles) embed a dynamic breadcrumb tied to Webflow variables.
5 Product markups. Each product page gets enriched markup: name, description, images, brand, category. This covers the standard soundproofing enclosures, server rack cabinets, and acoustic booths.
15 BlogPosting markups. Each Resources article embeds full markup: title, description, image, dates, author with photo and LinkedIn, publisher, category, keywords, language.
Any new product, article, or brand added to the CMS inherits the markup across all five languages. Zero manual work.
Breadcrumbs and UI: navigation designed for humans and crawlers alike
The MS Noise catalog is built around:
- 3 product families (enclosures, server rack cabinets, acoustic booths)
- 13 industries served (pharma, labs, defense, petrochemicals, food processing…)
- A dozen compatible equipment brands (Leybold, Edwards, Agilent, Pfeiffer…)
A buyer landing on a product page from an AI search needs to navigate back seamlessly: product → category → industry → equipment brand. Crawlers need the same cues.
Breadcrumbs now cover every strategic page, in two visual variants and five languages.
On the UI side, the work on specification lists solves the same problem: stop burying the key information (noise reduction, equipment compatibility, dimensions) under a wall of text. A clean first view, a clear hierarchy for search engines.
Impact on Google and AI visibility
In Google. Product and article pages can now surface rich results directly in search, consistent across the five markets. Better understood at crawl time, they rank more easily on precise technical queries like vacuum pump soundproof enclosure or acoustic server rack for data center.
In AI engines. When a lab manager or an IT project lead queries ChatGPT, Perplexity, or Gemini, MS Noise information is now structured to be extracted and cited with the right attributes: product, compatible brand, industry, language.
A setup that scales. That's where the real value of the project sits. Every new product, article, or brand added to the CMS generates its markup across all five languages, with zero manual work. The MS Noise team publishes. The website stays compliant.
3 takeaways
1. A technically clean website can still be unreadable to AI. The absence of structured data is invisible to the eye, yet it stops generative engines from extracting the right attributes. Clean code doesn't replace clean data exposure.
2. On a multilingual website, automation isn't optional. Rolling out 85 markups by hand across five languages would be a six-figure project and a maintenance nightmare. CMS wiring makes the work a one-off with long-term payoff.
3. Foundations have to hold on three fronts at once. Structured data, navigation, and product page UI all serve the same goal: making information extractable. Fixing one without the others leaves the blind spots wide open.
Want to get your website ready for Google and AI engines? Let's talk.
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